Recognizing Degraded Handwritten Characters

In this report, a character recognition system is proposed that handles degraded manuscript documents which were discovered at the St. Catherine’s Monastery. In contrast to state-of-the-art Ocr systems, no early decision, namely the image binarization, needs to be performed. Thus, an object recognition methodology is adapted for the recognition of ancient manuscripts. Therefore, interest points are extracted which allow for the computation of local descriptors. These are directly classified using a Svm with one against all tests. In order to localize characters, interest points that represent characters are found by means of a scale distribution histogram. Then, the remaining interest points are clustered using a k-means which is initialized with the previously selected interest points. Finally a voting scheme is applied where the local descriptors’ class probabilities are accumulated to a probability histogram for each character cluster. This histogram does not solely allow for a hard decision, but can be presented to human experts who can decide the character class for hardly readable characters according to the probabilities obtained. The system was evaluated on three different datasets, namely a synthetic with Latin script, degraded characters and real world data. The system achieves a F0.5 score of 0.77 on the last dataset mentioned.